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Structural Similarity-Based Object Tracking in Video Sequence

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Structural Similarity-Based Object Tracking in Video Sequence. / Loza, A.; Mihaylova, L.; Canagarajah, N. et al.
Information Fusion, 2006 9th International Conference on. 2006.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Loza, A, Mihaylova, L, Canagarajah, N & Bull, D 2006, Structural Similarity-Based Object Tracking in Video Sequence. in Information Fusion, 2006 9th International Conference on. Proceedings of the 9th International Conference on Information Fusion, Florence, Italy, 10/07/06. https://doi.org/10.1109/ICIF.2006.301574

APA

Loza, A., Mihaylova, L., Canagarajah, N., & Bull, D. (2006). Structural Similarity-Based Object Tracking in Video Sequence. In Information Fusion, 2006 9th International Conference on https://doi.org/10.1109/ICIF.2006.301574

Vancouver

Loza A, Mihaylova L, Canagarajah N, Bull D. Structural Similarity-Based Object Tracking in Video Sequence. In Information Fusion, 2006 9th International Conference on. 2006 doi: 10.1109/ICIF.2006.301574

Author

Loza, A. ; Mihaylova, L. ; Canagarajah, N. et al. / Structural Similarity-Based Object Tracking in Video Sequence. Information Fusion, 2006 9th International Conference on. 2006.

Bibtex

@inproceedings{976cc75f3d0b46028cda698c0d14d851,
title = "Structural Similarity-Based Object Tracking in Video Sequence",
abstract = "This paper addresses the problem of object tracking in video sequences. The use of a structural similarity measure for tracking is proposed. The measure reflects the distance between two images by comparing their structural and spatial characteristics and has shown to be robust to illumination and contrast changes. As a result it guarantees robustness of the tracking process under changes in the environment. The previously used Bhattacharyya distance is not robust to such changes. Additionally, when a tracker is run with the Bhattacharyya distance, histograms should be calculated in order to find the likelihood function of the measurements. With the new function there is no need to calculate histograms. A particle filter (PF) is implemented where this measure is used for computing the distance between the reference and current frame. The algorithm performance has been tested and evaluated over real-world video sequences, and has been shown to outperform methods based on colour and edge histograms.",
keywords = "Similarity measure, object tracking, video sequences, particle filtering, DCS-publications-id, inproc-452, DCS-publications-credits, dsp, DCS-publications-personnel-id, 121",
author = "A. Loza and L. Mihaylova and N. Canagarajah and D. Bull",
year = "2006",
month = jul,
day = "10",
doi = "10.1109/ICIF.2006.301574",
language = "English",
isbn = "1-4244-0953-5",
booktitle = "Information Fusion, 2006 9th International Conference on",
note = "Proceedings of the 9th International Conference on Information Fusion ; Conference date: 10-07-2006 Through 13-07-2006",

}

RIS

TY - GEN

T1 - Structural Similarity-Based Object Tracking in Video Sequence

AU - Loza, A.

AU - Mihaylova, L.

AU - Canagarajah, N.

AU - Bull, D.

PY - 2006/7/10

Y1 - 2006/7/10

N2 - This paper addresses the problem of object tracking in video sequences. The use of a structural similarity measure for tracking is proposed. The measure reflects the distance between two images by comparing their structural and spatial characteristics and has shown to be robust to illumination and contrast changes. As a result it guarantees robustness of the tracking process under changes in the environment. The previously used Bhattacharyya distance is not robust to such changes. Additionally, when a tracker is run with the Bhattacharyya distance, histograms should be calculated in order to find the likelihood function of the measurements. With the new function there is no need to calculate histograms. A particle filter (PF) is implemented where this measure is used for computing the distance between the reference and current frame. The algorithm performance has been tested and evaluated over real-world video sequences, and has been shown to outperform methods based on colour and edge histograms.

AB - This paper addresses the problem of object tracking in video sequences. The use of a structural similarity measure for tracking is proposed. The measure reflects the distance between two images by comparing their structural and spatial characteristics and has shown to be robust to illumination and contrast changes. As a result it guarantees robustness of the tracking process under changes in the environment. The previously used Bhattacharyya distance is not robust to such changes. Additionally, when a tracker is run with the Bhattacharyya distance, histograms should be calculated in order to find the likelihood function of the measurements. With the new function there is no need to calculate histograms. A particle filter (PF) is implemented where this measure is used for computing the distance between the reference and current frame. The algorithm performance has been tested and evaluated over real-world video sequences, and has been shown to outperform methods based on colour and edge histograms.

KW - Similarity measure

KW - object tracking

KW - video sequences

KW - particle filtering

KW - DCS-publications-id

KW - inproc-452

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1109/ICIF.2006.301574

DO - 10.1109/ICIF.2006.301574

M3 - Conference contribution/Paper

SN - 1-4244-0953-5

BT - Information Fusion, 2006 9th International Conference on

T2 - Proceedings of the 9th International Conference on Information Fusion

Y2 - 10 July 2006 through 13 July 2006

ER -